1,137,497 research outputs found

    On networks with side information

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    In this paper, we generalize the lossless coded side information problem from the three-node network of Ahlswede and K¨orner to more general network scenarios. We derive inner and outer bounds on the achievable rate region in the general network scenario and show that they are tight for some families of networks. Our approach demonstrates how solutions to canonical source coding problems can be used to derive bounds for more complex networks and reveals an interesting connection between networks with side information, successive refinement, and network coding

    On achievable rates for multicast in the presence of side information

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    We investigate the network source coding rate region for networks with multiple sources and multicast demands in the presence of side information, generalizing earlier results on multicast rate regions without side information. When side information is present only at the terminal nodes, we show that the rate region is precisely characterized by the cut-set bounds and that random linear coding suffices to achieve the optimal performance. When side information is present at a non-terminal node, we present an achievable region. Finally, we apply these results to obtain an inner bound on the rate region for networks with general source-demand structures

    Strategic Network Disruption and Defense

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    Networks are one of the essential building blocks of society. Not only do firms cooperate in R&D networks, but firms themselves may be seen as networks of information-exchanging workers. Social movements increasingly make use of networks to exchange information, just as on the negative side criminal and terrorist networks use them. However, the literature on networks has mainly focused on the cooperative side of networks and has so far neglected the competition side of networks. Networks themselves may face competition from actors with opposing interests to theirs. Several R&D networks may compete with one another. The firm as a network of employees obviously faces competition. In particular, given the importance of connectivity for networks, competing networks may try to disrupt each other, by trying to convince key players in competing networks to defect, or to stop sponsoring key links (strategic network disruption). In response, networks that face competition will adapt their structure, and will avoid vulnerable network structures. Such network competition is what our paper is concerned with.Strategic Network Disruption, Strategic Network Design, Noncooperative Network Games

    On Graph Stream Clustering with Side Information

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    Graph clustering becomes an important problem due to emerging applications involving the web, social networks and bio-informatics. Recently, many such applications generate data in the form of streams. Clustering massive, dynamic graph streams is significantly challenging because of the complex structures of graphs and computational difficulties of continuous data. Meanwhile, a large volume of side information is associated with graphs, which can be of various types. The examples include the properties of users in social network activities, the meta attributes associated with web click graph streams and the location information in mobile communication networks. Such attributes contain extremely useful information and has the potential to improve the clustering process, but are neglected by most recent graph stream mining techniques. In this paper, we define a unified distance measure on both link structures and side attributes for clustering. In addition, we propose a novel optimization framework DMO, which can dynamically optimize the distance metric and make it adapt to the newly received stream data. We further introduce a carefully designed statistics SGS(C) which consume constant storage spaces with the progression of streams. We demonstrate that the statistics maintained are sufficient for the clustering process as well as the distance optimization and can be scalable to massive graphs with side attributes. We will present experiment results to show the advantages of the approach in graph stream clustering with both links and side information over the baselines.Comment: Full version of SIAM SDM 2013 pape

    Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

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    Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM) 201
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